ETL Pipeline Generator

Data Ingestion for Knowledge Graphs

Raw records often come from CSV, JSON, databases, REST APIs, and streaming sources with inconsistent fields, unclear entity boundaries, and inferred relationships. Loading that data directly into a graph store can corrupt the schema and degrade queries. The ETL Pipeline Generator breaks the workflow into configurable, rerunnable Extract, Transform, and Load stages, reducing ad-hoc scripting and troubleshooting.

How It Works

It generates a structured pipeline design from data sources, transformation rules, target systems, and quality requirements:
- Extract: connect files, databases, APIs, warehouses, or streaming sources to form a raw data stream.
- Transform: apply entity detection, relationship inference, schema mapping, deduplication, type conversion, validation, and filtering to create graph-ready structures.
- Load: write to Neo4j, RDF Triple Stores, ArangoDB, TigerGraph, or CSV/JSON targets.

Key artifacts include YAML pipeline configuration, DAG definitions, Python/Scala/SQL scripts, data flow diagrams, monitoring and alerting configs, and documentation. The execution path typically covers config validation, connector initialization, extraction, transformation, quality checks, loading, load verification, and metrics collection.

Boundaries and Notes

This skill is best for pipeline design and configuration generation, especially for automated ingestion, knowledge graph population, and data quality validation. It does not replace business semantics: entity definitions, relationship rules, and constraints must still be defined by the team. For high-throughput streaming, complex permission isolation, or production-grade disaster recovery, combine it with Airflow, Spark, Great Expectations, and similar systems, and add testing, version control, and credential management.